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A mathematical model to simulate movement
For ‘attracting features’, such as nesting or roosting sites, we employ potential terms that are logarithmic in distance. Logarithmic potentials have been employed in diffusion models7 such as those involving long-range interactions8. The forces due to these are inversely proportional to distance from the features. Given a choice between locations, an animal would invariably drift towards ones that are closer. Additionally, they also command some influence for longer distances. We did consider alternatives such as a potential that corresponds to an inverse squared force but it diminishes much faster as the distance to the source increases. The ‘repulsive features’ such as human dominated areas are incorporated using Gaussian type potentials that would have an influence only when the animal is close to them. Such forces fall off exponentially fast as one goes away from the source location.
The corresponding Langevin equations can be written as:

$$begin{aligned} frac{dx}{dt}= & {} -gamma sum _{i}frac{ 2alpha times (x – x_{i})}{(x – x_{i})^2 + (y – y_{i})^2} nonumber \&+,gamma sum _{j}Big ((x-x_{j}) e^{-((x-x_{j})^2 + (y-y_{j})^2)}Big ) + root 2 of {2D}xi _x(t) end{aligned}$$
(1)

$$begin{aligned} frac{dy}{dt}= & {} -gamma sum _{i}frac{ 2alpha times (y – y_{i})}{(x – x_{i})^2 + (y – y_{i})^2} nonumber \&+,gamma sum _{j}Big ((y-y_{j}) e^{-((x-x_{j})^2 + (y-y_{j})^2)}Big ) + root 2 of {2D}xi _y(t) end{aligned}$$
(2)

where x and y denote the coordinates of an animal’s location. ((x_{i}), (y_{i})) and ((x_{j}), (y_{j})) denote locations of i attracting and j repelling features respectively. We only choose nests as points of attraction for breeding hornbills since their diurnal movements are strongly centred around the nests. The white noise terms (xi _x) and (xi _y) are Gaussian in nature and delta correlated—which means that no correlations exist between the noise values at different instances of time. (gamma ) and D denote the drift and diffusion coefficients respectively. The drift coefficient (gamma ) represents the directedness of motion, which could be interpreted as strength of bias towards/against certain features in the landscape. In contrast, D quantifies the strength of random undirected motion. The force term with coefficient (-gamma ) results from negative gradient of the logarithmic potential, whose choice we explained earlier:

$$begin{aligned} U = gamma sum _{i} log left{ (x – x_{i})^2 + (y – y_{i})^2 right} ^{alpha } ,,,. end{aligned}$$
(3)

The value of (alpha ) is determined from calculation of first passage times of the birds (discussed in the following section) and comparison of the values so obtained with observational (telemetry) data. We find that (alpha ) = 8 gives biologically sensible first passage times for hornbills (see “Calculating First Passage Times” in Methods section, Table 3 and Supplementary Tables 1, 2). If one observes an animal’s movement for a very long time, the probability of finding the animal would decrease more drastically away from a central feature for lower values of (alpha ). Such variations are captured by the steady-state probability distributions of space-use that we describe in the following section.
Fokker–Planck methods
Although the Langevin equations can generate trajectories of movement, the corresponding simulations need to be run for very long times to infer reliable information about spatial use. The time steps are further much smaller than the frequency of data recorded by the GPS. The step-lengths thus generated from simulated trajectories do not lend themselves to comparison against those from the recorded data. A convenient alternative is to solve a Fokker–Planck equation which has a direct correspondence with the Langevin equations. For our model, this takes the form:

$$begin{aligned} frac{ partial P(x,y,t)}{partial t}&= frac{partial }{partial x} left{ F_x + D frac{partial }{partial x} right} P(x,y,t) nonumber \&quad +, frac{partial }{partial y} left{ F_y + D frac{partial }{partial y}.right} P(x,y,t) end{aligned}$$
(4)

where

$$begin{aligned} F_x&= -gamma sum _{i} frac{ 2 alpha times (x – x_{i})}{(x – x_{i})^2 + (y – y_{i})^2} nonumber \&quad+, gamma sum _{j} (x – x_{j}) times e^{-( (x – x_{j})^2 + (y – y_{j})^2)} nonumber \ F_y&= -gamma sum _{i} frac{ 2 alpha times (y – y_{i})}{(x – x_{i})^2 + (y – y_{i})^2} nonumber \&quad+ gamma sum _{j} (y – y_{j}) times e^{-( (x – x_{j})^2 + (y – y_{j})^2)} end{aligned}$$
(5)

The Fokker–Planck equation describes the evolution of the probabilities of occurrence over a given region. The probability distribution eventually reaches a ‘steady state’ which captures the long-term occurrence probabilities for a given bird, and it does not change beyond this point in time. This steady-state probability distribution can be computed by setting the time derivative term to zero in Eq. (4). The numerical solution of the Fokker–Planck equation involves discretizing the spatial derivatives involved. The steady state probability distribution is consequently obtained on a spatial domain of discretized grids.
Interestingly, Giuggioli et al.9 considered logarithmic potentials in their work on home range estimation, where an exponent of 8 was found to have a very similar steady state distribution to that from a harmonic potential. Harmonic potential has been utilized in analyzing home ranges of Peromyscus maniculatus10.
Using the steady-state solution of the Fokker–Planck equation, we compute the mean square displacement averaged over different possible starting locations using the steady state distribution. A discrete version of the mean-square displacement (MSD) can be defined as:

$$begin{aligned} MSD = sum _i^N langle (x – x_i)^2 + (y – y_i)^2 rangle P_{0}(x_i,y_i) end{aligned}$$
(6)

where (P_0(x_i,y_i)) is the distribution of starting locations (x_i) and (y_i) from where displacements are calculated. The inner angular brackets represent a similar weighted average of the mid-points of all grids over the steady-state probability distribution (P_{text {st}}(x,y)). Many of the grids that we define to perform simulations lie outside the known home range of the birds. The probability of choosing a starting location is defined using a Gaussian distribution centred around the nest or the most visited roost site.
The square root of the MSD defines a characteristic length scale. This could be interpreted as home range length when the steady state distribution is computed over an infinite extent9. A logarithmic potential does not lend itself to such computations since it decays much more slowly such that the characteristic length continues to grow with the size of the area considered. We evaluate the characteristic length scale (L) on a domain that is not much larger in size compared to the observed home range.
We also calculate L from empirical data by using the probability of occurrence over space inferred from two-dimensional histograms of location data. The MSD in this case is evaluated in the same vein as above but now the displacements from initial locations are weighted over the probabilities of occurrence derived from the histograms. Since these probabilities are only available for each grid, we choose only the mid-points of grids as possible locations to find the result. The starting locations are chosen from a uniform distribution over the mid-points of the grids. This is definitely a crude way of evaluating L but it does give us some way of comparing our numerical solutions against data. Finding a joint-probability distribution over the two dimensions would have been ideal but it is complicated by the fact that the distribution over space is multi-modal owing to multiple roosts for some hornbills. When inferring MSD from the location coordinates directly, it increases before saturating as the sampling frequency is decreased. For very high sampling frequency (or very small time intervals), diffusion effects dominate which leads to an almost linear increase in MSD. The effects of drift are more prominent compared to diffusion for lower sampling frequencies which marks the saturation of the MSD values10.
A first-passage time model for heterogeneous environments
The temporal information about an animal’s whereabouts is highly scrambled in the data. An important quantity of interest that could be extracted from movement data is the search time to reach a given target. A very useful measure of search times is the ‘first passage time’. Very generally, first passage time is the time taken for a given state variable to reach a particular value. In the case of animal movement, it can be interpreted as the time taken to reach a particular target location. McKenzie et al.11 derived an interesting first passage time model which had a direct correspondence with a Fokker–Planck equation. We use the prescription of Moorcroft et al.12,13 to estimate the drift and diffusion coefficients. This assumes a movement kernel that is a product of exponential distribution of step lengths and von Mises distribution for the turning angles. (This may be seen in the “goodness of fit tests” section in Methods where we assess fit of our data to claimed distributions.) It can be expressed as:

$$begin{aligned} K({mathbf{X}} ,{mathbf{X}}’ ,tau )=, & {} frac{1}{rho } f_tau (rho ) k_tau (phi ) end{aligned}$$
(7)

$$begin{aligned} {rm{where}},,,,,,,,,,,, ,f_tau (rho )=, & {} lambda e^{-lambda rho }end{aligned}$$
(8)

$$begin{aligned} k_tau (phi )=, & {} frac{1}{2 pi I_0(kappa _tau )} exp [kappa _tau cos (phi )] end{aligned}$$
(9)

Here, ({mathbf{X}} ), ({mathbf{X}}’ ) denote the current and previous locations respectively, f is the exponential distribution of step lengths (rho ) with rate parameter (lambda ) and mean (bar{rho }_{tau } = 1/lambda ), and (k_{tau }) is the von Mises distribution of turning angles (phi ). (tau ) refers to the time taken to complete a given step. The turning angles are computed with respect to the nest/roost sites. (kappa _tau ) is the concentration parameter of the von Mises distribution which signifies the departure from a uniform distribution of movement directions. The normalizing factor (I_0(kappa _tau )) is a modified Bessel function of the first kind and of zeroth order. The drift and diffusion coefficients can be reliably estimated as:

$$begin{aligned} gamma= & {} lim _{tau rightarrow 0} frac{bar{rho }_{tau } kappa _tau }{2tau } end{aligned}$$
(10)

$$begin{aligned} D= & {} lim _{tau rightarrow 0} frac{bar{{rho _{tau }}^2}}{4tau } end{aligned}$$
(11)

Employing the formalism in McKenzie11 to derive the equation for the first passage time T, we obtain the following equation:

$$begin{aligned}&gamma sum _{i} left{ frac{ 2alpha times ({mathbf{X}} – {mathbf{X}} _{i})}{(x – x_{i})^2 + (y – y_{i})^2} right} cdot nabla T nonumber \&quad -, gamma sum _{j} left{ ({mathbf{X}} – {mathbf{X}} _{j}) e^{-( (x – x_{j})^2 + (y – y_{j})^2)} right} cdot nabla T nonumber \&quad +, D nabla ^2 T + 1 = 0 end{aligned}$$
(12)

The terms in dot product with (nabla T) are simply the drift coefficients with spatial dependence.
McKenzie et al.11 had a simpler version of the first passage time equation that only accounted for bias towards the home range centre. The authors mention that the task of solving the first passage time equation is computationally harder with terms that account for more complex types of heterogeneities. We transform the partial differential equation in (12) into polar coordinates which simplifies the process of solving it (see First Passage Time calculation in Methods). The first passage times obtained from this solution also help us fix the value of (alpha ) in the equation above and subsequently in the logarithmic potential in (3), and in Eqs. (1) and (2). On performing this analysis for different hornbills, we see that (alpha ) = 8 works very well for them irrespective of the species and distribution of heterogeneities around them (see First Passage Time calculation in Methods). First passage times are calculated from the roosting/nesting site that lies closest to the home range centre. In case of GHNBr2, we calculate the first passage times from the approximate home range centre where no roosts exist. This ensures that most points considered for computations lie within the actual extent of the bird’s recorded locations. We used the Minimum Convex Polygon method to estimate the approximate home range centre14. This helped in identifying a location for each bird—which was a roost/nest in most cases—from where first passage times were subsequently computed. The method used for home range estimation is not relevant in the context of our proposed model and results presented, and therefore we do not consider other alternatives. More

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Linjun Xie is a postdoctoral researcher studying urban sustainability and environmental governance at Durham University, UK.Credit: Samer Angelone

Postdoctoral researcher Linjun Xie reveals what an eco-island on the outskirts of Shanghai taught her about sustainable development in China
What is your research area?
I study urban sustainability and environmental governance at Durham University, UK, although I’ve been home in China throughout the pandemic.
In recent decades, rural areas close to megacities such as Shanghai, Beijing and Shenzhen have been absorbed into city development plans. Chongming is one such rural area, made up of three islands in the mouth of the Yangtze River, northeast of Shanghai. The Chongming Eco-Island Project is a municipal government scheme intended to be a model for more environmentally sustainable urban development in China.
These urban transitions can alter local landscapes and ecology, causing a loss of wildlife and natural habitats, as well as environmental pollution.
So in 2010, Shanghai announced its ambition to turn the Chongming district into a modern eco-island that would balance ecological sustainability with economic growth.
How did you end up researching the impact of sustainable development in China from the United Kingdom?
After completing my degree in urban planning at Huaqiao University in Xiamen, China, I was looking for places to research sustainable development. Cardiff University in the United Kingdom offers a year-long master’s course as part of its eco-cities research programme and I joined in 2015. During my course, I heard many references to how Chongming was different from other eco-projects and so when I applied to do my PhD at the University of Nottingham Ningbo in China, I asked if I could focus on it for my thesis.
What makes Chongming different from other sustainable development projects?
Chongming is unlike other state-led eco-projects in China, such as the Tianjin eco-city, a collaborative effort between Singapore and China, or the Shenzhen International Low Carbon City. These highly compact and modern cities are constructed on empty land or previous industrial sites. In these cities, everyone is a newcomer: there are no indigenous people.

The district of Chongming, by contrast, is an environment with high-quality rural land, diverse landscapes ranging from wetlands and crop fields to forests, and it is already home to nearly 700,000 people. Their homes are spread over a large area so it feels sparsely populated. Many people have lived on the islands their whole lives. This means that you can’t start from scratch. Policies need to be integrated into the local community and ecology.
You might wonder why plans to turn a rural community into an eco-project are necessary at all. The answer is that without protections, the area will not stay this way for long. The land is close to central Shanghai and so has development value. One part of the area is not part of this eco-project and you can see how quickly high-rise buildings have gone up.
In 2010, a list of goals were set by the Chongming district government, including limits on construction, the protection of arable land and an increase in forestation.
It also set social and economic targets, such as the implementation of clean-energy transportation, consolidation of the population into compact settlements, and development of the island’s green industries, such as organic farming and low-carbon manufacturing.
How successful is this project?
Statistics show that unbridled urbanization, which is common in China, has been reversed in Chongming. For instance, by 2016 the forest coverage on Chongming was 23%, twice the average of Shanghai.
A series of ecological tourism projects have been built, such as the Dongtan Wetland Park, and tourism revenue quadrupled from 2008 to 2016, rising from 270 million yuan (US$41.7 million) to 1.09 billion yuan.
In what areas could the project be improved?
Our research revealed some concerns. For example, the targets set in the eco-island plan also serve as key evaluation criteria for officials’ job performance. So they encourage the adoption of short-term measures that are not necessarily the best long-term solutions.
For example, to increase the amount of forest cover, extensive land has been turned into forest, but plantations of a single fast-growing tree species have been introduced that do not encourage or support local biodiversity.

Also, the aesthetics of the landscape are sometimes prioritized over the needs of local ecology and biodiversity: cement is often used, and uniformly landscaped riverbanks for river regulation are common. These are an attempt to improve the water quality in rivers but don’t support local wetland plants and aquatic species.
There is also the question of transport. Chongming is an attractive rural retreat for Shanghai residents and on weekends and during national holidays, the Shanghai Yangtze River Tunnel-Bridge, which directly connects the east of Chongming Island to central Shanghai, is often terribly congested.
Public transport needs to improve. Once you arrive on the island, there are electric buses and many tourists use bicycles, but cars are still more convenient when making the journey from Shanghai.
Chongming must find a balance between its economic and ecological interests. The region needs the money that comes from tourism. But to be truly sustainable it needs to become both self-reliant and environmentally secure.
In general, what needs to be done to achieve sustainable urban development?
Well-intentioned ecological initiatives can, in fact, have destructive effects if the locality is not completely understood. For example, in Chongming, government officials adopted a strategy called ‘one town, one tree species and one flower’, which meant that each local town needed to plant a different tree species and flower species in their respective jurisdiction.
The selection of plants was chosen at random from a list produced by the Chongming district government. Consequently, more than 20 species of trees were planted separately in each town, including imported varieties such as northern red oak (Quercus rubra) and red maple (Acer rubrum).
But this plan poses risks for biodiversity because new plants can destroy the natural connectivity between local species.
So it is crucial to foster connections between historians, ecologists, engineers, planners, policymakers and local communities when planning and building ecological development. More

Many cities in China, such as Xi’an (pictured), have experienced rapid growth in the past few decades.Credit: Xinhua/Shutterstock

On 28 January 2020, a team of Chinese conservation scientists distributed a questionnaire across social-media platforms, asking Chinese citizens how they felt about proposed legislation that would ban the consumption and trade of wildlife in the country.
It was an apposite moment: the questionnaire hit social-media platforms such as WeChat and Weibo just days after China had been forced to close its major cities to prevent the spread of a disease that scientists suspected was transferred to humans from an animal species at a market in Wuhan.
More than 90% of the 74,070 respondents were in favour of a complete ban on wildlife trade — and, a month later, the central government came to the same conclusion and legislated to that effect. Researchers are increasingly studying the impact of these policies, and the country’s biodiversity. But big questions remain about whether China will deliver on its growing list of environmental commitments.

Bin Zhao, an ecologist at Fudan University in Shanghai, China, says that, since the start of the COVID-19 pandemic, people in urban areas have been paying more attention to biodiversity than ever before. “People realized that contact with wild animals could lead to an outbreak of an epidemic, even in urban areas. This not only enhanced people’s understanding of biodiversity, but also promoted the idea that wildlife-protection law needed to be improved,” says Zhao.
It came at a time when China was already committed to changing its approach to ecological protection, he says. In 2018, China amended its constitution to include the goal of becoming an ‘ecological civilization’. In the words of Chinese President Xi Jinping in 2017, economic development could no longer be at the expense of the environment.
Multiple environmentally friendly policies have already been announced, such as the introduction of an ‘ecological red line’ policy to protect more of the Chinese mainland from development (see ‘Protected land’); a new network of national parks; stricter supervision of conservation; and a streamlining of environmental-oversight agencies — all to meet a government target of making the country’s environment ‘beautiful’ by 2035.

Sources: UN/Xinhua/OECD

Big cities, few controls
In 1950, about only 13% of China’s population lived in cities. But since the 1980s, the country’s cities have grown rapidly as the engines of its economic growth (see ‘Urban population’). Millions left homes in rural areas to forge more prosperous lives in growing and newly built cities. Government policies, aimed at bolstering the economy, helped to encourage close to two-thirds of China’s population to move to these new urban areas, and the nation continues to have one of the world’s fastest growing urban populations. This has put intense pressure on the country’s ecology.

Sources: UN/Xinhua/OECD

“From an economic perspective, our ecosystems and environment have historically been considered to be worthless,” says Zhao. China’s natural resources, such as its wetlands, forests and water sources, haven’t received the same level of care from authorities as targets for economic growth, he says (see ‘Vegetation change’).

Sources: UN/Xinhua/OECD

As urban areas grow, there are direct and indirect impacts on ecological systems, according to Rob McDonald, who researches the impact and dependencies of cities on the natural world at The Nature Conservancy in Washington DC.
Land is repurposed for development, and natural resources are needed to construct buildings and provide food and water for city dwellers, he says. These changes can lead to environmental problems, such as water and air pollution, insufficient water availability and deforestation much farther afield than in urban areas themselves.
China’s government has been open about its commitment to tackling these problems, says Alice Hughes, a zoologist at the Xishuangbanna Tropical Botanical Garden in Menglun town, China. In May 2021, China will host the fifteenth United Nations Convention on Biological Diversity, also known as COP 15, in Kunming, where 200 countries will meet to sign off on a legally binding set of global targets to protect the world’s biodiversity. The country has already contributed to some broader environmental targets, including being carbon neutral by 2060.
China has had some success, most notably in reducing air pollution. For example, in 2017, the amount of fine particulate matter in Beijing’s air dropped by just under 40% from the level in 2013, the year when national targets were launched.
But at a press conference to discuss China’s progress on ecological and environmental protection, Cui Shuhong, an official at the Ministry of Ecology and Environment, said the country has much more to do to alleviate the fundamental pressures placed on its natural resources by economic development.
Zhengguang Zhu, an environmental officer at China’s National Marine Environmental Monitoring Center, is familiar with preparations for COP 15: there are multiple working groups operating within the Ministry of Ecology and Environment, which are each responsible for different aspects of the event, from logistics to setting targets for improvements to China’s environment.

Live turtles on display at a wildlife market in Shanghai, China, in August 2020. During the COVID-19 pandemic, the Chinese government issued a policy banning wildlife trade for food, but trade of exotic animals as pets still continues.Credit: Ales Plavevski/EPA-EFE/Shutterstock

These working groups ask China’s public bodies, such as the ministry of agriculture, to offer their opinions on what the country feels should be included in the final roadmap for the coming decade.
“I think the meeting will show that China has done its homework and is willing to be a good host. But leadership is not just about hospitality. It’s about having an ambitious framework that enables change, and I think we’ve got a long way to go before that happens,” says Zhu.
Behaviour change
Conservation researcher Tien Ming Lee, based at the Sun Yat-sen University in Guangzhou, China, says scientists and politicians are currently focused on finding better ways to protect Chinese ecosystems while continuing the country’s urban economic growth.
His research team works across a range of projects, all focused on finding ways to prompt people to act differently and sustainably. For example, he is currently part of a 4-year, €10-million (US$12 million) project, mainly funded by the European Union, called Partners against Wildlife Crime. The project, which began in January 2019, hopes to disrupt the illicit supply chains through which exotic animals and plants, specifically tigers (Panthera tigris), Asian elephants (Elephas maximus), Siamese rosewood (Dalbergia cochinchinensis) and freshwater turtles, are traded throughout Cambodia, China, Laos, Malaysia, Myanmar, Thailand and Vietnam.
As part of this project, Lee’s team and Lishu Li at the Wildlife Conservation Society China Counter Wildlife Trafficking Program are developing marketing materials to change the buying habits of urban Chinese consumers by attempting to dissuade them from illegal acts, such as buying tiger bone or elephant skin online for jewellery and traditional medicine, or keeping endangered freshwater turtles as pets. Lee says the materials have been developed with behavioural-science techniques: they aim to appeal to consumers’ desire to be seen to act in a conscientious manner.

Police patrol the wetlands of the Yellow River Estuary ecotourism area near Dongying City, China.Credit: Costfoto/Barcroft Media via Getty

Lee has also been part of a research project that looked at how trade agreements that stem from the country’s international Belt and Road economic initiative, an infrastructure project that aims to link trade across Europe, Asia and Africa to China, could lead to a greater demand for traditional Chinese medicine across the world. The plant, animal and fungal products used in these practises are often sourced from the wild, which might exacerbate the illegal and unsustainable trade of those species, he says.
His research, a collaboration with Amy Hinsley, a conservation biologist at the University of Oxford, UK, concluded that there was a clear, urgent need for China to introduce carefully managed supply chains and ensure that rural farmers have resources for sustainable farming.
During her four-decade career, Lu Zhi, a conservation biologist at Peking University in Beijing, has seen a shift in her field’s focus. It moved from observing animals in their natural habitats and coming up with ways to protect them from human activity to observing human behaviour: studying what can be done to make people’s lives more ecologically sustainable.
In 2017, Zhi’s Shanshui Conservation Center, a non-governmental organization she founded in 2007 to develop community-based conservation projects, began working with herdsmen in Qinghai province on the Tibetan Plateau. The team wanted to help them to develop livelihoods from conservation activities in an underdeveloped, highly biodiverse area of China. The villagers learnt how to patrol and monitor wildlife, and how to act as guides for tourists interested in animal watching — including for the elusive and endangered snow leopard (Panthera uncia). Similar projects have been rolled out in 42 villages in western China.
Zhi admits that such small projects are certainly not enough to bring the paradigm shift needed to safeguard the country’s vulnerable ecosystems. Government intervention has proved to be effective in tackling the larger issues, such as air and water pollution, she says. But “it’s not fair to ask people in rural areas not to develop their lives for the sake of wildlife, while others live in prosperous cities. We need alternative solutions.” More

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The Sierra de Manantlán biosphere reserve in Mexico is a source of clean water for urban residents in nearby cities.Credit: Adriana Margarita Larios Arellano/Shutterstock

Sierra de Manantlán is a 140,000-hectare biosphere reserve in west central Mexico. It is home to 3,000 plant species and a forest whose soils and limestone mountains enable purified water to reach the nearby town of Colima.
Twenty years ago, researchers at the University of Guadalajara in Mexico proposed that Colima should consider paying to use the forest’s clean water, and that the money could go to supporting the biosphere reserve’s inhabitants.
The 30,000 people who lived in the forest were poor and in ill health. Unemployment was high, and there were few schools or medical clinics. But the absence of buildings, piped water and electric power had an unintended consequence: it was keeping the forest intact. In return for looking after nature, the researchers argued, the people of Sierra de Manantlán should be compensated, and the funds used for education, health care and employment training. Although not a new idea for Mexico, it was rejected by the city’s authorities. The concept that a forest ecosystem had monetary value — and that its custodians could be compensated — was controversial and much misunderstood.

Last week, however, countries took a giant step towards enabling public authorities to put a value on their environment. At its annual meeting, the United Nations Statistical Commission — whose members are responsible for setting and verifying standards for official statistics in their countries — laid out a set of principles for measuring ecosystem health and calculating a monetary value. These principles, known as the System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA), are set to be adopted by many countries on 11 March.
The principles were agreed after a 3-year writing and review process that involved 100 experts and 500 reviewers from various disciplines and countries. Once adopted, they will give national statisticians an internationally agreed rule book. It will provide a template for payments for ecosystem services — such as those once proposed for Colima — and an official benchmark against which the condition of ecosystems can be judged by policymakers and researchers over time.
The decision didn’t go as far as it might have done. The overwhelming majority of participating countries — led by Brazil, Colombia, India, Mexico and South Africa, among others — wanted the new rules to be designated as a statistical standard. These countries, rich in biodiversity, want to get on with valuing their natural systems, partly so that any ecological losses can be compared with potential gains from economic development. The designation of a statistical standard would also have enabled statistics offices to access public and international funding to carry out what would be regarded as a core part of their work, and not something voluntary or non-essential.
But the United States and a number of European Union countries objected. This was partly on the grounds that there is still much debate over valuation methodology, meaning that it is too soon to use ‘standard’ as a label. This setback was unfortunate: participating countries could have adopted the label while creating a system for revision and refinement, ensuring that the new standard could continue to be improved. Fortunately, the meeting’s attendees chose the next best thing — calling the rules “internationally recognized statistical principles and recommendations”.

The objections raised are a reminder that opinions on setting monetary values for nature are deeply held, with persuasive arguments on all sides. Some argue that nature is too valuable to be regarded in the same way as a commodity, and belongs to all. Valuation in the economic sense suggests that someone has ownership rights — but ecosystem services are rarely, if ever, ‘owned’ by anyone. The new principles do take this into account.
The record of the statisticians’ meeting shows that much debate remains on how to value something that isn’t bought and sold in a conventional way. But at the same time, this is an active area of research. Many studies have been captured in a landmark report, The Economics of Biodiversity: The Dasgupta Review, published last month by the UK Treasury. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services is also conducting a review of the concept of valuation, which will include additional perspectives from the humanities, and voices from under-represented communities, such as Indigenous peoples.
The debates will continue, but agreement between the world’s statisticians is nevertheless an important step. It means, for example, that those wishing to compensate low-income and marginalized communities for protecting nature — such as the communities in Sierra de Manantlán — now have an internationally agreed template to work from. And policymakers will have to contend with the heads of statistics agencies if they object. UN chief economist Elliot Harris rightly called the new principles a game changer. “The economy needs a bailout, but so does nature,” he said. “What we measure, we value, and what we value, we manage.” Momentum on valuing ecosystem services is now unstoppable, and that is a good thing. More

An improved reference genome of P. alba var. pyramidalis
To identify the major structural variation between the genomes of these two species, we first produced a chromosome-level genome assembly of P. alba var. pyramidalis using single-molecule sequencing and chromosome conformation capture (Hi-C) technologies, and then performed comparative genomic analysis with a recently published genome assembly of P. euphratica37. The resulting assembly of P. alba var. pyramidalis consisted of 131 contigs spanning 408.08 Mb, 94.74% (386.61 Mb) of which were anchored onto 19 chromosomes (Supplementary Fig. S1 and Supplementary Tables S1–S3). A total of 40,215 protein-coding genes were identified in this assembly (Supplementary Table S4). The content of repetitive elements in the genome of P. alba var. pyramidalis (138.17 Mb, 33.86% of the genome) is 188.94 Mb less than that of P. euphratica (327.11 Mb, 56.95% of the genome), which contributes greatly to their differences in genome size (Supplementary Table S5).
3D organization of the poplar genomes
To characterize the spatial organization and evolution of poplar 3D genomes at a high resolution, we performed Hi-C experiments using HindIII for P. euphratica and P. alba var. pyramidalis, generating a total of 482.95 million sequencing read pairs. These data were mapped to their respective reference genome sequences. After stringent filtering, 81.72 and 94.61 million usable valid read pairs were obtained in P. euphratica and P. alba var. pyramidalis, respectively, and used for subsequent comparative 3D genome analysis (Supplementary Table S6). In addition, we profiled the DNA methylation and transcriptomes of the same tissue samples to provide a framework for understanding the relationships among epigenetic features and 3D chromatin architecture in poplar.
We first examined genome packing at the chromosomal level with a genome-wide Hi-C map at 50 kb binning resolution for P. euphratica and P. alba var. pyramidalis. As expected, the normalized Hi-C map from both species showed intense signals on the main diagonal (Fig. 1, and Supplementary Figs. S2 and S3) and a rapid decrease in the frequency of intrachromosomal interactions with increasing genomic distance, indicating frequent interactions between sequences close to each other in the linear genome (Supplementary Fig. S4). Strong intrachromosomal and interchromosomal interactions were also observed on the chromosome arms, implying the presence of chromosome territories in the nucleus, in which each chromosome occupies a limited, exclusive nuclear space16,38.
Fig. 1: Hi-C heatmaps with compartment region analysis results at 50-kb resolution of P. euphratica chromosome 1 (left) and P. alba var. pyramidalis chromosome 1 (right).

The heatmaps at the top are Hi-C contact maps at 50-kb resolution, which show global patterns of chromatin interaction in the chromosome. The chromosome is shown from top to bottom and left to right. The ICE-normalized interaction intensity is shown on the color scale on the right side of the heatmap. The track below the Hi-C heatmap shows the partition of A (red histogram, PC1  > 0) and B (green histogram, PC1 5 kb) structural variants ranging from 5 to 446 kb in length in the alignment of the two genomes, including 719 inversions, 476 translocations, and 7947 and 10,093 unique regions in P. alba var. pyramidalis and P. euphratica, respectively (Supplementary Tables S10 and S11).
To characterize the relationship between structural variation and spatial organization of the poplar genomes, we first analyzed the conservation of A/B compartments between P. alba var. pyramidalis and P. euphratica, using a 50-kb Hi-C matrix. The results showed that 71.52% (145.75 Mb in P. euphratica and 145.63 Mb in P. alba var. pyramidalis) of the total length of the syntenic regions have the same compartment status between the two species, while 43.68 and 43.71 Mb of the genomic regions exhibit A/B compartment switching in P. alba var. pyramidalis and P. euphratica, respectively (Fig. 3a). For the regions with structural variation, we found that 77% of the inversion events between the two genomes had no effects on their compartment status, while 61% of the translocation events occurred within the regions exhibiting compartment switching (Fig. 4a and Supplementary Table S10). Moreover, we also found that 38.59% and 33.39% of the nonsyntenic regions were identified as A compartments in P. alba var. pyramidalis and P. euphratica, respectively, indicating that the large-scale insertions and/or deletions are biased to occur at heterochromatic regions (Fig. 4b). We further assessed the conservation of genome organization at the TAD level by examining whether the orthologous genes within the same TAD in one species could still be located within the TAD in another species19,21,23. The results indicated that only 48.04% of TADs from P. alba var. pyramidalis and 40.95% from P. euphratica were substantially shared between the two species (Figs. 3b, c). Taken together, these results indicated that the 3D genome organization shows surprisingly low conservation across poplar species at both the compartmental and TAD levels.
Fig. 3: Evolutionary conservation of compartment status and TADs across P. euphratica and P. alba var. pyramidalis.

a Overlap of compartment status between syntenic regions in P. euphratica and P. alba var. pyramidalis. b Overlap of TADs between syntenic regions in P. euphratica and P. alba var. pyramidalis. c Example of conserved TAD structures across a syntenic region between P. euphratica and P. alba var. pyramidalis. The TADs are outlined by black triangles in the heatmaps, and the position of the TAD domains is indicated by alternating blue-green line segments. The mean cf value used to identify the domains is also shown. The orthology tracks of these conserved domains are shown at the bottom

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Fig. 4: Relationship between structural variation and spatial organization of the genomes of P. euphratica and P. alba var. pyramidalis.

a Analysis of compartment inversion (left) and translocation (right) across P. euphratica and P. alba var. pyramidalis. b Analysis of compartments of species-specific regions in P. euphratica (left) and P. alba var. pyramidalis (right)

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Relationship between chromatin interactions and expression divergence of WGD-derived paralogs
Poplar species have undergone a recent WGD event followed by diploidization, a process of genome fractionation that leads to functional and expression divergence of the duplicated gene pairs27,28,33. Although no biased gene loss or expression dominance was found between the two poplar subgenomes, there is evidence that nearly half of the WGD-derived paralogs have diverged in expression32,33. To explore the potential role of chromatin dynamics on the observed expression patterns of duplicated genes, we examined their differences in chromatin interaction patterns for both species. We first identified a total of 10,438 and 9754 paralogous gene pairs showing interchromosomal interactions in P. euphratica and P. alba var. pyramidalis, respectively. After correlating the frequency of chromatin interactions with their differences in expression, we found that gene pairs with biased expression (more than twofold differences in expression levels) interacted less frequently than gene pairs with similar expression levels in both species (P = 1.71 × 10−6 and 7.20 × 10−7 for P. euphratica and P. alba var. pyramidalis, respectively, Mann–Whitney U test; Fig. 5a). We also estimated the interaction score (the average of the distance-normalized interaction frequencies) for bins involved in the paralogous gene pairs and quantified their differences in interaction strength (Supplementary Fig. S7 and Supplementary Table S12)3,23. Our results showed that for gene pairs with biased expression, highly expressed gene copies have stronger interaction strengths than weakly expressed copies (P = 2.10 × 10−12 and 2.74 × 10−2 for P. alba var. pyramidalis and P. euphratica, respectively, Mann–Whitney U test), while no significant differences were observed for gene pairs with similar expression levels (Fig. 5b). We further investigated these phenomena at the level of high-order chromatin architecture and found that the gene pairs located in conserved TADs had similar expression levels (P = 2.68 × 10−3 and 7.86 × 10−6 for P. euphratica and P. alba var. pyramidalis, respectively, Mann–Whitney U test; Supplementary Fig. S8). Overall, our analyses indicate that the extensive expression divergence between WGD-derived paralogs in Populus is associated with the differences in their chromatin dynamics and 3D genome organization, and suggest that this organization may function as a key regulatory layer underlying expression divergence during diploidization.
Fig. 5: Comparison of interaction levels between WGD-derived paralogs with biased/similar expression in P. euphratica and P. alba var. pyramidalis.

a The box plot shows that the interaction frequency of WGD-derived paralogs with biased (fold change  > 2) and similar (fold change  More